An Innovative Approach for Detecting Skin Diseases using Deep CNN Classifier Model

Abstract

Dermatology is one of the most unpredictable and challenging fields to diagnose, due to the intricacies involved in the process. In the field of dermatology, determining the potential skin problem a patient may be experiencing frequently requires doing thorough tests. Depending on the practitioner, the time may differ. This is also predicated on the individual's experience. Therefore, a system that is capable of diagnosing skin diseases without these limitations is required. We suggest an automated image-based approach that uses machine learning classification to identify skin conditions. This system will process, evaluate, and relegate the picture data according on different aspects of the photos by using computational techniques. Skin visuals are processed to improve the image quality and filtered to eliminate unwanted noise. Using sophisticated methods like Convolutional Neural Networks (CNNs), extract features from the image, categorize it using the Softmax classifier algorithm, and create a diagnosis report. This application will be an effective and reliable technique for the detection of dermatological diseases since it will produce results more quickly and with greater precision than the conventional method. Moreover, dermatology stream medical students can use this as a trustworthy real-time detection tool.